Achieving ‘Data Nirvana’: How to Maximise Business Value with Good Data Management – and Why it Really Matters

Introduction

Data is omnipresent in our daily lives. We are constantly generating and using it, both in our private activities and at work. And when it comes to organisations, it underpins almost every activity they undertake. Yet it’s so ubiquitous that we often take it for granted – or neglect to notice it’s even there.

Little wonder, therefore, that so many organisations fail to seize the huge opportunities that their data can deliver. Or, frequently, they may attempt to do so but find it too hard to achieve.

In our experience, one of the biggest stumbling blocks is nearly always poor data management. A failure to capture, store, organise and present data in ways that make it logical and easily useable can result in organisations missing out on huge amounts of business value – from greater efficiencies, to bottom-line improvements, and much more.

In this white paper, we’ll aim to help you tackle this challenge and move towards a state of ‘data nirvana’. We’ll show you the importance of good data management, the benefits it can deliver, and how to get there – not only from an IT service management (ITSM) point of view, but also from a holistic perspective that will help to create outstanding added value for your entire organisation. We hope you find this paper useful and would very much welcome your comments

Part 1: Why good data management really matters

Data is an incredibly powerful resource. One of the most powerful your organisation possesses. And it’s being generated by, and flowing through, every organisation all the time.

But the value it can bring will never magically appear out of nowhere. It can only have that power if you manage it correctly – and, in our experience, too many businesses fail to do this. The result is many missed golden opportunities, as well as countless lost hours and wasted pounds.

However, getting it right is well within the capabilities of any organisation. It just requires a good measure of focus, effort and expertise.

To do this, your data needs to be reliable, up-to-date, consistent and complete. What’s more, you need to ensure you’re collecting everything you need, in the most efficient and secure ways. You need to structure it in ways that are logical and can be understood, both by humans and machines (the latter being with automation in mind – see the next section for more). And, of course, you need to make it easily retrievable as and when it’s required.

And if you do get all this right, the business benefits can be huge. The biggest of all are likely to include more efficient operations and processes, the ability to make better-informed decisions that can impact on overall success within your organisation and improved bottom lines, and – maybe most important of all in the current technological landscape – pave the way for a smooth transition towards automation and the cost and efficiency benefits that can bring.

Part 2: The value-creating opportunities of data management

Good data management practices can open up a huge number of opportunities for organisations. Here, we’ll focus on three typical examples, although of course there are many more.

Automation

One of the most valuable is the ability to automate business processes, which can deliver multiple benefits, not least cost savings, increased efficiency and the opportunity to direct your people towards higher value tasks.

There are many business processes that can and should be automated, both withing the domain of ITSM and elsewhere. But doing it in an environment where data is managed poorly can make it difficult or impossible.

Put simply, for a system to be able to automate a process, it needs to be able to follow certain pre-determined steps. Those steps are triggered by a variety of things, but usually data.

So, for example: when a system can recognise that a particular status equals a certain value, it will be triggered to perform a specific action. And if an organisation is able to leverage its data to automate straightforward, logical process in this way, it’s already likely to be way ahead on its path to automation.

This would generally cover mundane, day-to-day tasks that require no human decision-making, and where there is no sensitivity or nuance required. All such tasks can and should be automated.

But it’s often not that simple. For it to work, the system needs to know what data it’s looking for, where to find it, and then what to do with it when it does find it. If an organisation can’t enable this, by truly understanding its data and structuring it in the appropriate ways, it is unlikely to be able to automate the task.

A good example is incident management. Say, for example, we want to automate the notification process around a major incident at a company. As soon as the helpdesk ticks the major incident flag, the company will want the major incident manager to be notified immediately.

That’s simple enough – in theory. But the system will need to know who the major incident manager is, and the answer may not be straightforward. It may, for example, depend on various conditions, such as the nature of the incident, the departments it affects, who is on annual leave, and so on.

Those conditions can be documented and added to the system – but it will need to know where to find the data. This might be on the HR (or another) system, rather than the IT system, which may be hard for the automation system to access and can lead to challenges with integration, data transfer, synchronisation and more… And all of a sudden, everything becomes very complicated and snowballs out of control.

That’s where sound data management practices can really help to simplify what you’re trying to do and make automated processes run as smoothly as possible.

Decision-making

This is an area where good, reliable data can really demonstrate its value. Yet, frequently, organisations do not realise quite how much of this they could have at their fingertips that can be translated into valuable information and insights.

For example, well-organised data can give organisations the ability to quickly and easily generate reports, create live dashboards, produce overviews for management, and so on.

This can lead to a culture of evidence-based decision-making which allows organisations to clearly understand where its priorities should lie, where to direct time and resources, and where underlying problems may exist.

It also helps with the velocity of decision-making, as well as the quality. Instead of using arguments based on opinions, proposals can be made and decisions approved rapidly, thanks to evidence that is based on solid data.

Metrics and KPIs

Related to the above, having in place sound metrics and key performance indicators (KPIs), that are rooted in solid data that is managed well, can help organisations to leverage technology in truly transformational ways.

It’s so often the case that an organisation will invest large budgets and countless hours into a new IT system, with a clear idea of the benefits they want to see and the value they want to add. But, once the system is deployed, it may never be measured.

So, for example, how many hours a month did that time-saving technology actually save? If organisations don’t collect the data to measure this, it’s likely to be harder to put forward a viable case for further investment in this, or any other, technology.

It also helps with understanding whether it’s worth embarking on more complex undertakings. By understanding the KPIs of a project and the benefits it’s likely to deliver in the first instance, it becomes easier to evaluate whether the cost and effort will be worthwhile. For example, it would rarely be worth taking hundreds of work hours to deliver a result that saves an organisation 10 minutes of effort per week.

Part 3: Why data management must be a cross-functional priority

The more data management is considered a major priority across an organisation, the more effectively it will be able to run, making better-informed decisions and delivering improved performance overall.

There are occasions where a business process will exist across only one function, but in general that’s rare. So, in most cases, there will be users from multiple functions, both upstream and downstream, who are relying on the same data flowing through the process.

It’s important, therefore, that when the data is created at the start of the process, it’s done in a way that can be used further downstream in the organisation at different times and for different purposes.

A good example is the data relating to a student at a university. It will be created as part of the admissions process, but then required for registration, teaching, graduation and potentially many other reasons during their time at the institution. If the admissions team do not record the data in a way that can be used later on by other functions, it can effectively be useless and will need to be re-captured at later stages – potentially leading to wasted hours and a frustrated student.

The crucial goal, therefore, is not to work in silos. Here, it can be helpful to take a holistic overview of data management from a business process design perspective. That way, an organisation can build an understanding of the way data flows through it – where it comes from, where it goes to next, who needs to use it, and what it may be used for.

Then, making data flow seamlessly through an organisation can be as simple as changing the way it’s captured and recorded in the first instance, or it may require a more substantial rethink of some of the ways the organisation designs and manages its processes.

This can, on occasion, present big challenges, largely because people are used to thinking in functional areas and their priorities may not extend to supporting other teams within the organisation.

An example here would be a typical ITSM system that needs to pull in data from other functions to identify users – which will give crucial information such as job title, function, location, and so on. If this kind of data flow can be joined up seamlessly so a helpdesk person can quickly understand who they’re helping, what their role is and where they’re located, it can save IT teams countless hours (and endless frustration on the end user’s part).

The necessary data for this may need to come from three or more other systems, such as HR, Facilities, Finance, and so on. And ensuring that all these functional areas are working together on sound data management practices can be challenging.

An ideal solution is to have a specific data function that manages corporate data and cuts across functions. However, that can require significant investment and be hard to achieve, thus often only being suitable for larger organisations.

A powerful alternative can be to put a project ‘wrap’ around data management. A project manager takes charge, but they also – crucially – bring in key people from every function. That way, everyone has a stake in the project’s success, which can engender closer co-operation and help to prevent conflict between functions.

This approach can be particularly helpful with migration or implementation projects, where everyone has skin in the game.

Alongside this, a useful tactic can be to explain the impact of poor data management by finding relatable examples that affect each function and demonstrating the benefits of doing it well. An effective way of doing this is to spend time with each function to understand their pain points and show how much better things could be if they were done differently and with their co-operation.

And, of course, it’s important to ensure you have the support of the most senior management in the organisation – and the best means of achieving this is to demonstrate the overall business benefits and bottom-line improvements that can be delivered thanks to better data management.

Part 4: Data management best practices

For data to be truly valuable to an organisation, it needs to:

The last of these points is particularly important and relies on business processes being designed or adapted to collect required data at the right times and in the right ways so that it can be truly useful across the organisation.

But, in addition to this, good data management is not only about the existing data that’s in front of your people. It’s about understanding what data your organisation might need right now to function better, and what it might need in the future as the business expands and evolves. That requires a clear understanding of how to capture this data now and in the future, as well as how it will be stored.

Then, sitting alongside this, access is a crucial issue. Data can be effectively worthless unless the people who need it can perform the tasks they need to do with it, when they need to do it.

These days, most organisations create masses of data all the time. But far too many are sitting on top of all this potentially useful information and locking it away. That may be due to inefficiencies and a lack of organisation, or often because of fears around compliance issues, GDPR and so on. A lot of organisations tend to err on the side of caution, when they might not necessarily need to.

One way to overcome this is to consider data from a role-based perspective. This requires some up-front consideration from the relevant management teams, who can ascertain what data a particular role will require access to in order to do their job effectively. This kind of approach, if done correctly, should not throw up any compliance issues.

An obvious example would be of someone working at an IT service desk. They may need access to specific HR data, such as an employee’s role and location in the building, in order to offer the correct level of IT support. But they will not need access to that employee’s salary details or their performance management record.

Such a system of role-based access is readily achievable, but it does require a certain amount of thought and effort in the first place in order to establish the correct protocols. This may initially appear daunting and unnecessarily time-consuming, but as with all good data management practices, it can ultimately lead to greater benefits for the organisation, such as time saving and improved service quality.

Part 5: Using data to maximise the value of ITSM tools

When it comes to implementing ITSM tools, and extracting maximum value from your investment in them, data is the spinal column on which they rely.

An important first principle is that most ITSM systems on the market these days are either good or excellent. However, they are only as effective as their implementation – and even more crucially, they are only as effective as the data that supports them and flows through them.

That’s why it’s vital for ITSM teams to implement and maintain good data management practices in order to ensure they extract maximum value from the tools in which they have invested.

There are two areas in particular that are worth focusing on: configuration management databases (CMDBs) and service catalogues.

CMDBs

Although many organisations do not have one, we strongly advise them to embrace the idea of a CMDB.

To understand why, imagine the following scenarios.

Either of these are examples that we have witnessed in real-world contexts, leading to disastrous consequences for the business concerned.

Often what happens here is a kind of organisational ‘butterfly effect’ – named after the imagined phenomenon of a butterfly flapping its wings in one place, leading to a storm arising elsewhere. In other words, by turning off or reconfiguring something in one place, there can be unintended or unknown consequences in another.

One way of mitigating against circumstances like this is to establish and maintain a CMDB, a document that maps out all the relationships between all of an organisation’s hardware assets and its services and processes. That way, you can clearly understand the effect of performing a specific action in a specific area and how it will impact the rest of the business, allowing you to take alleviating action or find a workaround.

For example, in that flooding scenario, you would be able to warn the parts of the business that will be impacted and help them find alternative solutions. And for the payroll server, you’d have understood in the first place that a better day to perform maintenance would have been the one after payroll day and not the one before.

Naturally, creating a CMDB involves a lot of time and effort, which can be off-putting for many organisations unwilling or unable to apply the necessary resource. There is also an understandable fear of it being ‘too difficult’ and not getting everything right.

However, neither of these should be reasons avoid doing it, as the consequences of not having a CMDB in place can potentially be business-critical. Above all, it’s important not to make ‘perfect’ the enemy of ‘good’. So, an organisation may not end up with a CMDB that is 100 per cent accurate – but something that is close to the truth is always likely to be more useful than nothing at all. The more an IT team knows about its organisation’s systems, the more it can do if problems arise, or to avoid the problems in the first place – and it enables them to focus on the areas they don’t know enough about.

Service catalogues

A service catalogue that accurately lists all the services that an organisation offers to its users might not be as data rich as a CMDB, but it is equally essential.

Done properly, it will not only categorise all the services available, it will also demonstrate how they can be bundled together and who is able to access them, based on accurate data around roles and functions.

This can help in a wide range of areas, a good example being the ability to rapidly on-board new starters. In just a few clicks, the system would know what hardware and software they require, what information needs to be loaded on their access cards, and so on.

It can also support with the move towards a greater level of self-service, again helping to save time and improve efficiencies within an organisation.

Part 6: Preparing data for a system implementation, migration or integration

Whenever we work with an organisation – whether it’s on an implementation, migration or integration – one of the most effective ways they can ensure they gain maximum value from the work we do with them is to ensure that their data is in good order prior to starting the project.

Therefore, here are some actions we recommend all organisations take before embarking on any work of this kind. What’s more these are good principles to adhere to under any circumstances.

Start with the process

Good data management doesn’t start with the data. It starts with the business processes that an organisation undertakes.

Based on this logic, some key questions to ask – and have clear answers to – are:

Collecting data in ways that make it useable

One of the first things we’re likely to request from an organisation we’re working with is data. But often they are unable to provide it in a way that is helpful.

A common area where many organisations fall down is by having free text fields when capturing data. A classic example might be as follows:

We need to know how many people in the organisation are UK citizens and how many are from other countries. If the form that asks the user for their nationality allows free text, a user from the UK is likely to write a wide variety of responses, including ‘U.K.’, ‘UK’, ‘Great Britain’, ‘Britain’, ‘G.B.’, ‘GB’, ‘England’, ‘Wales’, ‘Scotland’, and so on – where, in effect, they all fall into the same data category of UK citizen.

This wide variety of responses makes it hard to analyse the data; whereas having a simple drop-down menu that allows the user to choose from a pre-defined set of answers – so, in this case, ‘UK’ – would solve the problem at a stroke.

Using such principles to simplify and standardise data capture can help to save large amounts of time and minimise wasted expenditure.

Be prepared to move away from homegrown data systems

Often when we ask for data from an organisation – such as a list of locations and the users at each one – we’ll be informed that it’s all contained on an Excel spreadsheet. But that spreadsheet may be incomplete, as, for example, some of the necessary data is owned by other functions, such as HR. Or that spreadsheet may not be the only version of the truth – in other words, there could be multiple spreadsheets in existence, each one containing a slightly different version of the data.

A similar scenario might be an organisation having four versions of an Access database – one for HR, one for payroll, one for IT and one for facilities. This might all have been fine once, but if the business wants to truly leverage the power of its data to speed up processes and gain efficiencies, the data needs to be managed in a way that allows it to flow more freely.

We will never condemn an organisation for operating in this way, as it’s likely to be a legacy situation rather than a deliberate decision. Instead, we’ll take the time to speak to relevant stakeholders and understand the reasons for operating in this way – in other words, what are they attempting to achieve? That way, we can work with them towards effective solutions that optimise outcomes for them and the wider organisation.

A key principle here is to make everything as uncomplicated as possible, and to start with what an organisation already has to see what can be achieved with it.

However, it’s also crucial to understand that just doing nothing will usually lead to poor outcomes. Embarking on a journey towards ‘data nirvana’ can seem daunting, but it doesn’t have to be. Just moving one step at a time can help to create momentum and shift an organisation towards the benefits it needs to add value and remain competitive.

That’s not to negate the fact that doing this right can – and should – be a long-term commitment that will require considerable time and effort. The fear of failing or making a mistake can often be a blocker – but with the right support from people with deep expertise, that can easily be overcome.

Conclusion

In our experience, only a small percentage of organisations excel at data management. So, if this white paper leaves you feeling disheartened and overwhelmed, we can assure you that you’re not alone.

The good news is that fixing your data management problems, and reaping the benefits of getting everything into good shape, is not the impossible challenge it may seem at first. Yes, it will require effort and dedication. But with a methodical approach and the right support, you can deliver the outcomes you’re looking for – and maybe faster and more easily than you expected.

The most important thing is to just make a start. Small steps in the right direction, when carefully calculated, can reap rewards and set your organisation on a path towards its own ‘data nirvana’.

We’d love to help you make that start – so get in touch now and let us know your thoughts on this white paper. We’d be delighted to have a no-strings conversation with you about how we can use our decades of experience to help.